Russell Poldrack

Albert Ray Lang Professor of Psychology and Professor, by courtesy, of Computer Science

Bio

I grew up in a small town in Texas and attended Baylor University. After completing my PhD in experimental psychology at the University of Illinois in Urbana-Champaign, I spent four years as a postdoc at Stanford. I have held faculty positions at Massachusetts General Hospital/Harvard Medical School, UCLA, and the University of Texas. I joined the Stanford faculty in 2014.

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Current Research and Scholarly Interests

Our lab uses the tools of cognitive neuroscience to understand how decision making, executive control, and learning and memory are implemented in the human brain. We also develop neuroinformatics tools and resources to help researchers make better sense of data.

Abstract

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions; however, it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of fMRI data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.

Abstract

Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.

Abstract

It is believed that choice behavior reveals the underlying value of goods. The subjective values of stimuli can be changed through reward-based learning mechanisms as well as by modifying the description of the decision problem, but it has yet to be shown that preferences can be manipulated by perturbing intrinsic values of individual items. Here we show that the value of food items can be modulated by the concurrent presentation of an irrelevant auditory cue to which subjects must make a simple motor response (i.e., cue-approach training). Follow-up tests showed that the effects of this pairing on choice lasted at least 2 months after prolonged training. Eye-tracking during choice confirmed that cue-approach training increased attention to the cued items. Neuroimaging revealed the neural signature of a value change in the form of amplified preference-related activity in ventromedial prefrontal cortex.

Abstract

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights.

Abstract

A common goal of neuroimaging research is to use imaging data to identify the mental processes that are engaged when a subject performs a mental task. The use of reasoning from activation to mental functions, known as "reverse inference," has been previously criticized on the basis that it does not take into account how selectively the area is activated by the mental process in question. In this Perspective, I outline the critique of informal reverse inference and describe a number of new developments that provide the ability to more formally test the predictive power of neuroimaging data.

Abstract

Repeated study improves memory, but the underlying neural mechanisms of this improvement are not well understood. Using functional magnetic resonance imaging and representational similarity analysis of brain activity, we found that, compared with forgotten items, subsequently remembered faces and words showed greater similarity in neural activation across multiple study in many brain regions, including (but not limited to) the regions whose mean activities were correlated with subsequent memory. This result addresses a longstanding debate in the study of memory by showing that successful episodic memory encoding occurs when the same neural representations are more precisely reactivated across study episodes, rather than when patterns of activation are more variable across time.

Abstract

Previous work has shown that human adolescents may be hypersensitive to rewards, but it is not known which aspect of reward processing is responsible for this. We separated decision value and prediction error signals and found that neural prediction error signals in the striatum peaked in adolescence, whereas neural decision value signals varied depending on how value was modeled. This suggests that heightened dopaminergic prediction error responsivity contributes to adolescent reward seeking.

Abstract

Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes are organized in the brain. Using a variety of classifier techniques, we achieved cross-validated classification accuracy of 80% across individuals (chance = 13%). Using a neural network classifier, we recovered a low-dimensional representation common to all the cognitive-perceptual tasks in our data set, and we used an ontology of cognitive processes to determine the cognitive concepts most related to each dimension. These results revealed a small organized set of large-scale networks that map cognitive processes across a highly diverse set of mental tasks, suggesting a novel way to characterize the neural basis of cognition.

Abstract

Contemporary models of episodic memory posit that remembering involves the reenactment of encoding processes. Although encoding-retrieval similarity has been consistently reported and linked to memory success, the nature of neural pattern reinstatement is poorly understood. Using high-resolution fMRI on human subjects, our results obtained clear evidence for item-specific pattern reinstatement in the frontoparietal cortex, even when the encoding-retrieval pairs shared no perceptual similarity. No item-specific pattern reinstatement was found in the ventral visual cortex. Importantly, the brain regions and voxels carrying item-specific representation differed significantly between encoding and retrieval, and the item specificity for encoding-retrieval similarity was smaller than that for encoding or retrieval, suggesting different nature of representations between encoding and retrieval. Moreover, cross-region representational similarity analysis suggests that the encoded representation in the ventral visual cortex was reinstated in the frontoparietal cortex during retrieval. Together, these results suggest that, in addition to reinstatement of the originally encoded pattern in the brain regions that perform encoding processes, retrieval may also involve the reinstatement of a transformed representation of the encoded information. These results emphasize the constructive nature of memory retrieval that helps to serve important adaptive functions.SIGNIFICANCE STATEMENT Episodic memory enables humans to vividly reexperience past events, yet how this is achieved at the neural level is barely understood. A long-standing hypothesis posits that memory retrieval involves the faithful reinstatement of encoding-related activity. We tested this hypothesis by comparing the neural representations during encoding and retrieval. We found strong pattern reinstatement in the frontoparietal cortex, but not in the ventral visual cortex, that represents visual details. Critically, even within the same brain regions, the nature of representation during retrieval was qualitatively different from that during encoding. These results suggest that memory retrieval is not a faithful replay of past event but rather involves additional constructive processes to serve adaptive functions.

Abstract

The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide significance level (P<5 × 10(-8)). Gene-based analysis identified an additional three Bonferroni-corrected significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e.=0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.

Abstract

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Abstract

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

Abstract

Multivoxel pattern analysis (MVPA) has gained enormous popularity in the neuroimaging community over the past few years. At the group level, most MVPA studies adopt an "information based" approach in which the sign of the effect of individual subjects is discarded and a non-directional summary statistic is carried over to the second level. This is in contrast to a directional "activation based" approach typical in univariate group level analysis, in which both signal magnitude and sign are taken into account. The transition from examining effects in one voxel at a time vs. several voxels (univariate vs. multivariate) has thus tacitly entailed a transition from directional to non-directional signal definition at the group level. While a directional group-level MVPA approach implies that individuals have similar multivariate spatial patterns of activity, in a non-directional approach each individual may have a distinct spatial pattern. Using an experimental dataset, we show that directional and non-directional group-level MVPA approaches uncover distinct brain regions with only partial overlap. We propose a method to quantify the degree of spatial similarity in activation patterns over subjects. Applied to an auditory task, we find higher values in auditory regions compared to control regions.

Abstract

Processing speed is impaired in patients with psychosis, and deteriorates as a function of normal aging. These observations, in combination with other lines of research, suggest that psychosis may be a syndrome of accelerated aging. But do patients with psychosis perform poorly on tasks of processing speed for the same reasons as older adults? Fifty-one patients with psychotic illnesses and 90 controls with similar mean IQ (aged 19-69 years, all African American) completed a computerized processing-speed task, reminiscent of the classic digit-symbol coding task. The data were analyzed using the drift-diffusion model (DDM), and Bayesian inference was used to determine whether psychosis and aging had similar or divergent effects on the DDM parameters. Psychosis and aging were both associated with poor performance, but had divergent effects on the DDM parameters. Patients had lower information-processing efficiency ("drift rate") and longer nondecision time than controls, and psychosis per se did not influence response caution. By contrast, the primary effect of aging was to increase response caution, and had inconsistent effects on drift rate and nondecision time across patients and controls. The results reveal that psychosis and aging influenced performance in different ways, suggesting that the processing-speed impairment in psychosis is more than just accelerated aging. This study also demonstrates the potential utility of computational models and Bayesian inference for finely mapping the contributions of cognitive functions on simple neurocognitive tests.

Abstract

The ability to inhibit unwanted responses is critical for effective control of behavior, and inhibition failures can have disastrous consequences in real-world situations. Here, we examined how prior exposure to negative emotional stimuli affects the response-stopping network. Participants performed the stop-signal task, which relies on inhibitory control processes, after they viewed blocks of either negatively emotional or neutral images. In Experiment 1, we found that neural activity was reduced following negative image viewing. When participants were required to inhibit responding after neutral image viewing, we observed activation consistent with previous studies using the stop-signal task. However, when participants were required to inhibit responding after negative image viewing, we observed reductions in the activation of ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, medial frontal cortex, and parietal cortex. Furthermore, analysis of neural connectivity during stop-signal task blocks indicated that across participants, emotion-induced changes in behavioral performance were associated with changes in functional connectivity, such that greater behavioral impairment after negative image viewing was associated with greater weakening of connectivity. In Experiment 2, we collected behavioral data from a larger sample of participants and found that stopping performance was impaired after negative image viewing, as seen in longer stop-signal reaction times. The present results demonstrate that negative emotional events can prospectively disrupt the neural network supporting response inhibition.

Abstract

Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h(2) = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts.

Abstract

Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training.

Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attentionPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICAShine, J. M., Koyejo, O., Poldrack, R. A.2016; 113 (35): 9888-9891

Abstract

Little is currently known about the coordination of neural activity over longitudinal timescales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 mo. These temporal states were associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and also related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing additional supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience.

Abstract

Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.

Abstract

The administration of behavioral and experimental paradigms for psychology research is hindered by lack of a coordinated effort to develop and deploy standardized paradigms. While several frameworks (Mason and Suri, 2011; McDonnell et al., 2012; de Leeuw, 2015; Lange et al., 2015) have provided infrastructure and methods for individual research groups to develop paradigms, missing is a coordinated effort to develop paradigms linked with a system to easily deploy them. This disorganization leads to redundancy in development, divergent implementations of conceptually identical tasks, disorganized and error-prone code lacking documentation, and difficulty in replication. The ongoing reproducibility crisis in psychology and neuroscience research (Baker, 2015; Open Science Collaboration, 2015) highlights the urgency of this challenge: reproducible research in behavioral psychology is conditional on deployment of equivalent experiments. A large, accessible repository of experiments for researchers to develop collaboratively is most efficiently accomplished through an open source framework. Here we present the Experiment Factory, an open source framework for the development and deployment of web-based experiments. The modular infrastructure includes experiments, virtual machines for local or cloud deployment, and an application to drive these components and provide developers with functions and tools for further extension. We release this infrastructure with a deployment (http://www.expfactory.org) that researchers are currently using to run a set of over 80 standardized web-based experiments on Amazon Mechanical Turk. By providing open source tools for both deployment and development, this novel infrastructure holds promise to bring reproducibility to the administration of experiments, and accelerate scientific progress by providing a shared community resource of psychological paradigms.

Abstract

NeuroVault.org is dedicated to storing outputs of analyses in the form of statistical maps, parcellations and atlases, a unique strategy that contrasts with most neuroimaging repositories that store raw acquisition data or stereotaxic coordinates. Such maps are indispensable for performing meta-analyses, validating novel methodology, and deciding on precise outlines for regions of interest (ROIs). NeuroVault is open to maps derived from both healthy and clinical populations, as well as from various imaging modalities (sMRI, fMRI, EEG, MEG, PET, etc.). The repository uses modern web technologies such as interactive web-based visualization, cognitive decoding, and comparison with other maps to provide researchers with efficient, intuitive tools to improve the understanding of their results. Each dataset and map is assigned a permanent Universal Resource Locator (URL), and all of the data is accessible through a REST Application Programming Interface (API). Additionally, the repository supports the NIDM-Results standard and has the ability to parse outputs from popular FSL and SPM software packages to automatically extract relevant metadata. This ease of use, modern web-integration, and pioneering functionality holds promise to improve the workflow for making inferences about and sharing whole-brain statistical maps.

Abstract

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Abstract

Cue-approach training has been shown to effectively shift choices for snack food items by associating a cued button-press motor response to particular food items. Furthermore, attention was biased toward previously cued items, even when the cued item is not chosen for real consumption during a choice phase. However, the exact mechanism by which preferences shift during cue-approach training is not entirely clear. In three experiments, we shed light on the possible underlying mechanisms at play during this novel paradigm: (1) Uncued, wholly predictable motor responses paired with particular food items were not sufficient to elicit a preference shift; (2) Cueing motor responses early - concurrently with food item onset - and thus eliminating the need for heightened top-down attention to the food stimulus in preparation for a motor response also eliminated the shift in food preferences. This finding reinforces our hypothesis that heightened attention at behaviorally relevant points in time is key to changing choice behavior in the cue-approach task; (3) Crucially, indicating choice using eye movements rather than manual button presses preserves the effect, thus demonstrating that the shift in preferences is not governed by a learned motor response but more likely via modulation of subjective value in higher associative regions, consistent with previous neuroimaging results. Cue-approach training drives attention at behaviorally relevant points in time to modulate the subjective value of individual items, providing a mechanism for behavior change that does not rely on external reinforcement and that holds great promise for developing real world behavioral interventions.

Abstract

The occurrence of collinearity in fMRI-based GLMs (general linear models) may reduce power or produce unreliable parameter estimates. It is commonly believed that orthogonalizing collinear regressors in the model will solve this problem, and some software packages apply automatic orthogonalization. However, the effects of orthogonalization on the interpretation of the resulting parameter estimates is widely unappreciated or misunderstood. Here we discuss the nature and causes of collinearity in fMRI models, with a focus on the appropriate uses of orthogonalization. Special attention is given to how the two popular fMRI data analysis software packages, SPM and FSL, handle orthogonalization, and pitfalls that may be encountered in their usage. Strategies are discussed for reducing collinearity in fMRI designs and addressing their effects when they occur.

Abstract

Bias occurs in perceptual decisions when the reward associated with a particular response dominates the sensory evidence in support of a choice. However, it remains unclear how this bias is acquired and once acquired, how it influences perceptual decision processes in the brain. We addressed these questions using model-based neuroimaging in a motion discrimination paradigm where contextual cues suggested which one of two options would receive higher rewards on each trial. We found that participants gradually learned to choose the higher-rewarded option in each context when making a perceptual decision. The amount of bias on each trial was fit well by a reinforcement-learning model that estimated the subjective value of each option within the current context. The brain mechanisms underlying this bias acquisition process were similar to those observed in reward-based decision tasks: prediction errors correlated with the fMRI signals in ventral striatum, dlPFC, and parietal cortex, whereas the amount of acquired bias correlated with activity in ventromedial prefrontal (vmPFC), dorsolateral frontal (dlPFC), and parietal cortices. Moreover, psychophysiological interaction analysis revealed that as bias increased, functional connectivity increased within multiple brain networks (dlPFC-vmPFC-visual, vmPFC-motor, and parietal-anterior-cingulate), suggesting that multiple mechanisms contribute to bias in perceptual decisions through integration of value processing with action, sensory, and control systems. These provide a novel link between the neural mechanisms underlying perceptual and economic decision-making.

If all your friends jumped off a bridge: The effect of others' actions on engagement in and recommendation of risky behaviors.Journal of experimental psychology. GeneralHelfinstein, S. M., Mumford, J. A., Poldrack, R. A.2015; 144 (1): 12-17

Abstract

There is a large gap between the types of risky behavior we recommend to others and those we engage in ourselves. In this study, we hypothesized that a source of this gap is greater reliance on information about others' behavior when deciding whether to take a risk oneself than when deciding whether to recommend it to others. To test this hypothesis, we asked participants either to report their willingness to engage in a series of risky behaviors themselves; their willingness to recommend those behaviors to a loved one; or, how good of an idea it would be for either them or a loved one to engage in the behaviors. We then asked them to evaluate those behaviors on criteria related to the expected utility of the risk (benefits, costs, and likelihood of costs), and on engagement in the activity by people they knew. We found that, after accounting for effects of perceived benefit, cost, and likelihood of cost, perceptions of others' behavior had a dramatically larger impact on participants' willingness to engage in a risk than on their willingness to recommend the risk or their prescriptive evaluation of the risk. These findings indicate that the influence of others' choices on risk-taking behavior is large, direct, cannot be explained by an economic utility model of risky decision-making, and goes against one's own better judgment. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Abstract

It is common in the cognitive neuroscience literature to explain differences in activation in terms of differences in the "efficiency" of neural function. I argue here that this usage of the concept of efficiency is empty and simply redescribes activation differences rather than providing a useful explanation of them. I examine a number of possible explanations for differential activation in terms of task performance, neuronal computation, neuronal energetics, and network organization. While the concept of "efficiency" is vacuous as it is commonly employed in the neuroimaging literature, an examination of brain development in the context of neural coding, neuroenergetics, and network structure provides a roadmap for future investigation, which is fundamental to an improved understanding of developmental effects and group differences in neuroimaging signals.

Abstract

The amount of shared data available for re-analysis has greatly increased in the last few years. Here we discuss some of the challenges raised by the analysis of these shared datasets and propose some strategies to address these issues.

Abstract

Here we present NeuroVault-a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.

Abstract

A prerequisite for a pattern analysis using functional magnetic resonance imaging (fMRI) data is estimating the patterns from time series data, which then are input into the pattern analysis. Here we focus on how the combination of study design (order and spacing of trials) with pattern estimator impacts the Type I error rate of the subsequent pattern analysis. When Type I errors are inflated, the results are no longer valid, so this work serves as a guide for designing and analyzing MVPA studies with controlled false positive rates. The MVPA strategies examined are pattern classification and similarity, utilizing single trial activation patterns from the same functional run. Primarily focusing on the Least Squares Single and Least Square All pattern estimators, we show that collinearities in the models, along with temporal autocorrelation, can cause false positive correlations between activation pattern estimates that adversely impact the false positive rates of pattern similarity and classification analyses. It may seem intuitive that increasing the interstimulus interval (ISI) would alleviate this issue, but remaining weak correlations between activation patterns persist and have a strong influence in pattern similarity analyses. Pattern similarity analyses using only activation patterns estimated from the same functional run of data are susceptible to inflated false positives unless trials are randomly ordered, with a different randomization for each subject. In other cases, where there is any structure to trial order, valid pattern similarity analysis results can only be obtained if similarity computations are restricted to pairs of activation patterns from independent runs. Likewise, for pattern classification, false positives are minimized when the testing and training sets in cross validation do not contain patterns estimated from the same run.

Abstract

Patients with schizophrenia perform poorly on cognitive skill learning tasks. This study is the first to investigate the neural basis of impairment in cognitive skill learning in first-degree adolescent relatives of patients with schizophrenia. We used functional magnetic resonance imaging to compare activation in 16 adolescent siblings of patients with childhood-onset schizophrenia (COS) and 45 adolescent controls to determine whether impaired cognitive skill learning in individuals with genetic risk for schizophrenia was associated with specific patterns of neural activation. The siblings of patients with COS were severely impaired on the Weather Prediction Task (WPT) and showed a relative deactivation in frontal regions and in the striatum after extensive training on the WPT compared with controls. These differences were not accounted for by performance differences in the 2 groups. The results suggest that corticostriatal dysfunction may be part of the liability for schizophrenia.

Abstract

The stop-signal task, in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision making, a drift-diffusion model of simple decisions was fitted to stop-signal task data from go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the go stimulus correlated with greater activation in the right frontal pole for both go and stop trials. On stop trials, stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and BG. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology.

Abstract

How categories are represented continues to be hotly debated across neuroscience and psychology. One topic that is central to cognitive research on category representation but underexplored in neurobiological research concerns the internal structure of categories. Internal structure refers to how the natural variability between-category members is coded so that we are able to determine which members are more typical or better examples of their category. Psychological categorization models offer tools for predicting internal structure and suggest that perceptions of typicality arise from similarities between the representations of category members in a psychological space. Inspired by these models, we develop a neural typicality measure that allows us to measure which category members elicit patterns of activation that are similar to other members of their category and are thus more central in a neural space. Using an artificial categorization task, we test how psychological and physical typicality contribute to neural typicality, and find that neural typicality in occipital and temporal regions is significantly correlated with subjects' perceptions of typicality. The results reveal a convergence between psychological and neural category representations and suggest that our neural typicality measure is a useful tool for connecting psychological and neural measures of internal category structure.

Abstract

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data.

Abstract

Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels.

Abstract

Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task vs. a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading) and a highly practiced one (plain word reading), allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by 2 weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror vs. plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

Abstract

Sexual dimorphism in the brain and cognition is a topic of widespread interest. Many studies of sex differences have focused on visuospatial and verbal abilities, but few studies have investigated sex differences in executive functions. We examined two key components of executive function - response inhibition and response monitoring - in healthy men (n = 285) and women (n = 346) performing the Stop-signal task. In this task, participants are required to make a key press to a stimulus, unless a tone is presented at some delay following the initial stimulus presentation; on these infrequent trials, participants are instructed to inhibit their planned response. Response inhibition was assessed with an estimate of the latency needed to inhibit a response (stop-signal reaction time), and response monitoring was measured by calculating the degree to which participants adjusted their reaction times based on the immediately preceding trial (e.g., speeding following correct trials and slowing following errors). There were no sex differences in overall accuracy or response inhibition, but women showed greater sensitivity to trial history. Women sped up more than men following correct 'Go' trials, and slowed down more than men following errors. These small but statistically significant effects (Cohen's d = 0.25-0.3) suggest more flexible adjustments in speed-accuracy trade-offs in women and greater cognitive flexibility associated with the responsive control of action.

Abstract

Studies of adults with attention-deficit/hyperactivity disorder (ADHD) have suggested that they have deficient response inhibition, but findings concerning the neural correlates of inhibition in this patient population are inconsistent. We used the Stop-Signal task and functional magnetic resonance imaging (fMRI) to compare neural activation associated with response inhibition between adults with ADHD (N=35) and healthy comparison subjects (N=62), and in follow-up tests to examine the effect of current medication use and symptom severity. There were no differences in Stop-Signal task performance or neural activation between ADHD and control participants. Among the ADHD participants, however, significant differences were associated with current medication, with individuals taking psychostimulants (N=25) showing less stopping-related activation than those not currently receiving psychostimulant medication (N=10). Follow-up analyses suggested that this difference in activation was independent of symptom severity. These results provide evidence that deficits in inhibition-related neural activation persist in a subset of adult ADHD individuals, namely those individuals currently taking psychostimulants. These findings help to explain some of the disparities in the literature, and advance our understanding of why deficits in response inhibition are more variable in adult, as compared with child and adolescent, ADHD patients.

Abstract

In our TICS Review in 2004, we proposed that a sector of the right inferior frontal cortex (rIFC) in humans is critical for inhibiting response tendencies. Here we survey new evidence, discuss ongoing controversies, and provide an updated theory. We propose that the rIFC (along with one or more fronto-basal-ganglia networks) is best characterized as a brake. This brake can be turned on in different modes (totally, to outright suppress a response; or partially, to pause), and in different contexts (externally, by stop or salient signals; or internally, by goals). We affirm inhibition as a central component of executive control that relies upon the rIFC and associated networks, and explain why rIFC disruption could generally underpin response control disorders.

Abstract

The ability to adjust bias, or preference for an option, allows for great behavioral flexibility. Decision bias is also important for understanding cognition as it can provide useful information about underlying cognitive processes. Previous work suggests that bias can be adjusted in 2 primary ways: by adjusting how the stimulus under consideration is processed, or by adjusting how the response is prepared. The present study explored the experimental, behavioral, and theoretical distinctions between these biases. Different bias manipulations were employed in parallel across perceptual and memory-based decisions to assess the generality of the 2 biases. This is the 1st study to directly test whether conceptually similar bias instructions can induce dissociable bias effects across different decision tasks. The results show that stimulus and response biases can be separately induced in both tasks, suggesting that the biases generalize across different types of decisions. When analyzing behavioral data, the 2 biases can be differentiated by focusing on the time course of bias effects and/or by fitting choice reaction time models to the data. These findings have strong theoretical implications about how observed bias relates to underlying cognitive processes and how it should be used when testing cognitive theories. Guidelines are presented to help researchers identify how to induce the biases experimentally, how to dissociate them in the behavioral data, and how to quantify them using drift diffusion models. Because decision bias is pervasive across many domains of cognitive science, these guidelines can be useful for future work exploring decision bias and choice preferences.

Abstract

We studied healthy, first-degree relatives of patients with schizophrenia to test the hypothesis that deficits in cognitive skill learning are associated with genetic liability to schizophrenia. Using the Weather Prediction Task (WPT), 23 healthy controls and 10 adult first-degree Relatives Of Schizophrenia (ROS) patients were examined to determine the extent to which cognitive skill learning was automated using a dual-task paradigm to detect subtle impairments in skill learning. Automatization of a skill is the ability to execute a task without the demand for executive control and effortful behavior and is a skill in which schizophrenia patients possess a deficit. ROS patients did not differ from healthy controls in accuracy or reaction time on the WPT either during early or late training on the single-task trials. In contrast, the healthy control and ROS groups were differentially affected during the dual-task trials. Our results demonstrate that the ROS group did not automate the task as well as controls and continued to rely on controlled processing even after extensive practice. This suggests that adult ROS patients may engage in compensatory strategies to achieve normal levels of performance and support the hypothesis that impaired cognitive skill learning is associated with genetic risk for schizophrenia.

Abstract

To overcome unhealthy behaviors, one must be able to make better choices. Changing food preferences is an important strategy in addressing the obesity epidemic and its accompanying public health risks. However, little is known about how food preferences can be effectively affected and what neural systems support such changes. In this study, we investigated a novel extensive training paradigm where participants chose from specific pairs of palatable junk food items and were rewarded for choosing the items with lower subjective value over higher value ones. In a later probe phase, when choices were made for real consumption, participants chose the lower-valued item more often in the trained pairs compared with untrained pairs. We replicated the behavioral results in an independent sample of participants while they were scanned with fMRI. We found that, as training progressed, there was decreased recruitment of regions that have been previously associated with cognitive control, specifically the left dorsolateral pFC and bilateral parietal cortices. Furthermore, we found that connectivity of the left dorsolateral pFC was greater with primary motor regions by the end of training for choices of lower-valued items that required exertion of self-control, suggesting a formation of a stronger stimulus-response association. These findings demonstrate that it is possible to influence food choices through training and that this training is associated with a decreasing need for top-down frontoparietal control. The results suggest that training paradigms may be promising as the basis for interventions to influence real-world food preferences.

Abstract

The sharing of data is essential to increasing the speed of scientific discovery and maximizing the value of public investment in scientific research. However, the sharing of human neuroimaging data poses unique ethical concerns. We outline how data sharing relates to the Belmont principles of respect-for-persons, justice, and beneficence. Whereas regulators of human subjects research often view data sharing solely in terms of potential risks to subjects, we argue that the principles of human subject research require an analysis of both risks and benefits, and that such an analysis suggests that researchers may have a positive duty to share data in order to maximize the contribution that individual participants have made.

Abstract

The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels.

Abstract

Despite a national reduction in the prevalence of cigarette smoking, ~19% of the adult US population persists in this behavior, with the highest prevalence among 18-25-year-olds. Given that the choice to smoke imposes a known health risk, clarification of brain function related to decision-making, particularly involving risk-taking, in smokers may inform prevention and smoking cessation strategies.This study aimed to compare brain function related to decision-making in young smokers and nonsmokers.The Balloon Analogue Risk Task (BART) is a computerized risky decision-making task in which participants pump virtual balloons, each pump associated with an incremental increase in potential payoff on a given trial but also with greater risk of balloon explosion and loss of payoff. We used this task to compare brain activation associated with risky decision-making in smokers (n = 18) and nonsmokers (n = 25), while they performed the BART during functional magnetic resonance imaging (fMRI). The participants were young men and women, 17-21 years of age.Risk level (number of pumps) modulated brain activation in the right dorsolateral and ventrolateral prefrontal cortices more in smokers than in nonsmokers, and smoking severity (Heaviness of Smoking Index) was positively related to this modulation in an adjacent frontal region.Given evidence for involvement of the right dorsolateral and ventrolateral prefrontal cortices in inhibitory control, these findings suggest that young smokers have a different contribution of prefrontal cortical substrates to risky decision-making than nonsmokers. Future studies are warranted to determine whether the observed neurobiological differences precede or result from smoking.

Abstract

One central goal in cognitive neuroscience of learning and memory is to characterize the neural processes that lead to long-lasting episodic memory. In addition to the stronger frontoparietal activity, greater category- or item-specific cortical representation during encoding, as measured by pattern similarity (PS), is also associated with better subsequent episodic memory. Nevertheless, it is unknown whether frontoparietal activity and cortical PS reflect distinct mechanisms. To address this issue, we reanalyzed previous data (Xue G, Dong Q, Chen C, Lu ZL, Mumford JA, Poldrack RA. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science. 330:97, Experiment 3) using a novel approach based on combined activation-based and information-based analyses. The results showed that across items, stronger frontoparietal activity was associated with greater PS in distributed brain regions, including those where the PS was predictive of better subsequent memory. Nevertheless, the item-specific PS was still associated with later episodic memory after controlling the effect of frontoparietal activity. Our results suggest that one possible mechanism of frontoparietal activity on episodic memory encoding is via enhancing PS, resulting in more unique and consistent input to the medial temporal lobe. In addition, they suggest that PS might index additional processes, such as pattern reinstatement as a result of study-phase retrieval, that contribute to episodic memory encoding.

Abstract

The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.

Abstract

Research on cognition often leads to debates that are centered on how many processes exist and how they interact to guide behavior. These debates occur across a range of domains and are often difficult to resolve with behavioral data because similar behavioral predictions can be made by models with different core assumptions. Such model mimicry limits researchers' ability to find differential support for one type of model over the other using behavioral data alone. We argue that functional neuroimaging can help overcome this problem by providing additional dependent measures to constrain model testing. Recent advances in analysis, like multivariate approaches, expand the amount and type of data available for model testing. We illustrate the benefits of this approach by highlighting imaging results that directly speak to the debate over the nature of recollection processes in memory. These results show how functional neuroimaging can advance studies of cognition by providing richer data sets for contrasting cognitive models.

Abstract

Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making tasks vary in their details. We conducted an fMRI investigation of the Angling Risk Task (ART), which is an improved behavioral measure of risky decision-making. In order to examine whether the observed pattern of neural activation was specific to the ART or generalizable, we also examined correlates of the Balloon Analog Risk Taking (BART) task in the same sample of 23 healthy adults. Exploratory analyses were conducted to examine the relationship between neural activation, performance, impulsivity and self-reported risk-taking. While activation in a valuation network was associated with reward tracking during the ART but not the BART, increased fronto-cingulate activation was seen during risky choice trials in the BART as compared to the ART. Thus, neural activation during risky decision-making trials differed between the two tasks, and this observation was likely driven by differences in task parameters, namely the absence vs. presence of ambiguity and/or stationary vs. increasing probability of loss on the ART and BART, respectively. Exploratory association analyses suggest that sensitivity of neural response to the magnitude of potential reward during the ART was associated with a suboptimal performance strategy, higher scores on a scale of dysfunctional impulsivity (DI) and a greater likelihood of engaging in risky behaviors, while this pattern was not seen for the BART. Our results suggest that the ART is decomposable and associated with distinct patterns of neural activation; this represents a preliminary step toward characterizing a behavioral measure of risky decision-making that may support a better understanding of naturalistic risk-taking.

Abstract

Recently, there has been a dramatic increase in the number of functional magnetic resonance imaging studies seeking to answer questions about how the brain represents information. Representational questions are of particular importance in connecting neuroscientific and cognitive levels of analysis because it is at the representational level that many formal models of cognition make distinct predictions. This review discusses techniques for univariate, adaptation, and multivoxel analysis, and how they have been used to answer questions about content specificity in different regions of the brain, how this content is organized, and how representations are shaped by and contribute to cognitive processes. Each of the analysis techniques makes different assumptions about the underlying neural code and thus differ in how they can be applied to specific questions. We also discuss the many pitfalls of representational analysis, from the flexibility in data analysis pipelines to emergent nonrepresentational relationships that can arise between stimuli in a task.

Abstract

Patients with schizophrenia show deficits in skill learning. We tested the hypothesis that impaired skill learning is associated with liability for schizophrenia by determining if it is present in non-affected siblings of patients. This study examined cognitive skill learning in adolescent siblings of patients with childhood onset schizophrenia (COS), who are at high genetic risk for the disorder, and age-matched controls. A probabilistic classification task was used to assess cognitive skill learning, which has been shown to be impaired in patients with striatal dysfunction or schizophrenia. Differences between the groups emerged within the first 50 trials of training: the controls showed significant learning while the COS siblings did not. Furthermore, after extended training over 800 additional trials the siblings of COS probands reached a lower level of asymptotic performance than controls. These results suggest that a behavioral impairment in probabilistic classification learning in healthy, unaffected siblings mirrors the deficits seen in patients and thus may reflect genetic liability for the disease.

Abstract

A critical component of decision making is the ability to adjust criteria for classifying stimuli. fMRI and drift diffusion models were used to explore the neural representations of perceptual criteria in decision making. The specific focus was on the relative engagement of perceptual- and decision-related neural systems in response to adjustments in perceptual criteria. Human participants classified visual stimuli as big or small based on criteria of different sizes, which effectively biased their choices toward one response over the other. A drift diffusion model was fit to the behavioral data to extract estimates of stimulus size, criterion size, and difficulty for each participant and condition. These parameter values were used as modulated regressors to create a highly constrained model for the fMRI analysis that accounted for several components of the decision process. The results show that perceptual criteria values were reflected by activity in left inferior temporal cortex, a region known to represent objects and their physical properties, whereas stimulus size was reflected by activation in occipital cortex. A frontoparietal network of regions, including dorsolateral prefrontal cortex and superior parietal lobule, corresponded to the decision variables resulting from the downstream stimulus-criterion comparison, independent of stimulus type. The results provide novel evidence that perceptual criteria are represented in stimulus space and serve as inputs to be compared with the presented stimulus, recruiting a common network of decision regions shown to be active in other simple decisions. This work advances our understanding of the neural correlates of decision flexibility and adjustments of behavioral bias.

Abstract

Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.

Abstract

Despite growing interest in multi-voxel pattern analysis (MVPA) methods for fMRI, a major problem remains--that of generating estimates in rapid event-related (ER) designs, where the BOLD responses of temporally adjacent events will overlap. While this problem has been investigated for methods that reduce each event to a single parameter per voxel (Mumford et al., 2012), most of these methods make strong parametric assumptions about the shape of the hemodynamic response, and require exact knowledge of the temporal profile of the underlying neural activity. A second class of methods uses multiple parameters per event (per voxel) to capture temporal information more faithfully. In addition to enabling a more accurate estimate of ER responses, this allows for the extension of the standard classification paradigm into the temporal domain (e.g., Mourão-Miranda et al., 2007). However, existing methods in this class were developed for use with block and slow ER data, and there has not yet been an exploration of how to adapt such methods to data collected using rapid ER designs. Here, we demonstrate that the use of multiple parameters preserves or improves classification accuracy, while additionally providing information on the evolution of class discrimination. Additionally, we explore an alternative to the method of Mourão-Miranda et al. tailored to use in rapid ER designs that yields equivalent classification accuracies, but is better at unmixing responses to temporally adjacent events. The current work paves the way for wider adoption of spatiotemporal classification analyses, and greater use of MVPA with rapid ER designs.

Abstract

Over the last 20 years, fMRI has revolutionized cognitive neuroscience. Here I outline a vision for what the next 20 years of fMRI in cognitive neuroscience might look like. Some developments that I hope for include increased methodological rigor, an increasing focus on connectivity and pattern analysis as opposed to "blobology", a greater focus on selective inference powered by open databases, and increased use of ontologies and computational models to describe underlying processes.

Abstract

Higher levels of impulsivity have been implicated in the development of alcohol use disorders. Recent findings suggest that impulsivity is not a unitary construct, highlighted by the diverse ways in which the various measures of impulsivity relate to alcohol use outcomes. This study simultaneously tested the following dimensions of impulsivity as determinants of alcohol use and alcohol problems: risky decision making, self-reported risk-attitudes, response inhibition, and impulsive decision making.Participants were a community sample of nontreatment seeking problem drinkers (n = 158). Structural equation modeling (SEM) analyses employed behavioral measures of impulsive decision making (delay discounting task [DDT]), response inhibition (stop signal task [SST]), and risky decision making (Balloon Analogue Risk Task [BART]), and a self-report measure of risk-attitudes (domain-specific risk-attitude scale [DOSPERT]), as predictors of alcohol use and of alcohol-related problems in this sample.The model fits well, accounting for 38% of the variance in alcohol problems, and identified 2 impulsivity dimensions that significantly loaded onto alcohol outcomes: (i) impulsive decision making, indexed by the DDT; and (ii) risky decision making, measured by the BART.The impulsive decision-making dimension of impulsivity, indexed by the DDT, was the strongest predictor of alcohol use and alcohol pathology in this sample of problem drinkers. Unexpectedly, a negative relationship was found between risky decision making and alcohol problems. The results highlight the importance of considering the distinct facets of impulsivity to elucidate their individual and combined effects on alcohol use initiation, escalation, and dependence.

Abstract

Impulsive behavior is thought to reflect a traitlike characteristic that can have broad consequences for an individual's success and well-being, but its neurobiological basis remains elusive. Although striatal dopamine D₂-like receptors have been linked with impulsive behavior and behavioral inhibition in rodents, a role for D₂-like receptor function in frontostriatal circuits mediating inhibitory control in humans has not been shown. We investigated this role in a study of healthy research participants who underwent positron emission tomography with the D₂/D₃ dopamine receptor ligand [¹⁸F]fallypride and BOLD fMRI while they performed the Stop-signal Task, a test of response inhibition. Striatal dopamine D₂/D₃ receptor availability was negatively correlated with speed of response inhibition (stop-signal reaction time) and positively correlated with inhibition-related fMRI activation in frontostriatal neural circuitry. Correlations involving D₂/D₃ receptor availability were strongest in the dorsal regions (caudate and putamen) of the striatum, consistent with findings of animal studies relating dopamine receptors and response inhibition. The results suggest that striatal D₂-like receptor function in humans plays a major role in the neural circuitry that mediates behavioral control, an ability that is essential for adaptive responding and is compromised in a variety of common neuropsychiatric disorders.

Abstract

Multivariate pattern analysis (MVPA) has recently received increasing attention in functional neuroimaging due to its ability to decode mental states from fMRI signals. However, questions remain regarding both the empirical and conceptual relationships between results from MVPA and standard univariate analyses. In the current study, whole-brain univariate and searchlight MVPAs of parametric manipulations of monetary gain and loss in a decision making task (Tom et al., 2007) were compared to identify the differences in the results across these methods and the implications for understanding the underlying mental processes. The MVPA and univariate results did identify some overlapping regions in whole brain analyses. However, an analysis of consistency revealed that in many regions the effect size estimates obtained from MVPA and univariate analysis were uncorrelated. Moreover, comparison of sensitivity showed a general trend towards greater sensitivity to task manipulations by MVPA compared to univariate analysis. These results demonstrate that MVPA methods may provide a different view of the functional organization of mental processing compared to univariate analysis, wherein MVPA is more sensitive to distributed coding of information whereas univariate analysis is more sensitive to global engagement in ongoing tasks. The results also highlight the need for better ways to integrate these methods.

Abstract

We examined whether anterior and posterior hippocampal subregions in humans show distinct relationships to state and trait anxiety. In rodents, the ventral (but not dorsal) hippocampus is critically involved in contextual anxiety, whereas dorsal hippocampus is affected by chronic stress and genetically bred trait anxiety. These studies suggest that state forms of anxiety may be more associated with anterior (ventral in rodents) hippocampus, whereas trait forms of anxiety maybe more associated with posterior (dorsal in rodents) hippocampus. Participants were placed under alternating blocks of threat of shock and safety conditions while performing a secondary task, and state and trait anxiety measures were obtained. Using subject-specific anatomically defined masks, we found that state anxiety was related to activity in anterior but not posterior hippocampus, whereas trait anxiety showed the opposite pattern. Additionally, a psychophysiological connectivity analysis showed that activity in anterior hippocampus was more strongly related to activity in ventromedial prefrontal cortex under threat than under safety conditions, significantly more so than activity in posterior hippocampus was. Hence, anterior hippocampus shows a distinct moment-to-moment connectivity profile with other neural regions during threat relative to posterior hippocampus. The findings provide several lines of evidence for functional differentiation of anterior and posterior hippocampal involvement across state and trait components of anxiety in humans.

Abstract

Use of multivoxel pattern analysis (MVPA) to predict the cognitive state of a subject during task performance has become a popular focus of fMRI studies. The input to these analyses consists of activation patterns corresponding to different tasks or stimulus types. These activation patterns are fairly straightforward to calculate for blocked trials or slow event-related designs, but for rapid event-related designs the evoked BOLD signal for adjacent trials will overlap in time, complicating the identification of signal unique to specific trials. Rapid event-related designs are often preferred because they allow for more stimuli to be presented and subjects tend to be more focused on the task, and thus it would be beneficial to be able to use these types of designs in MVPA analyses. The present work compares 8 different models for estimating trial-by-trial activation patterns for a range of rapid event-related designs varying by interstimulus interval and signal-to-noise ratio. The most effective approach obtains each trial's estimate through a general linear model including a regressor for that trial as well as another regressor for all other trials. Through the analysis of both simulated and real data we have found that this model shows some improvement over the standard approaches for obtaining activation patterns. The resulting trial-by-trial estimates are more representative of the true activation magnitudes, leading to a boost in classification accuracy in fast event-related designs with higher signal-to-noise. This provides the potential for fMRI studies that allow simultaneous optimization of both univariate and MVPA approaches.

Abstract

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

Abstract

Response inhibition plays a critical role in adaptive functioning and can be assessed with the Stop-signal task, which requires participants to suppress prepotent motor responses. Evidence suggests that this ability to inhibit a prepotent motor response (reflected as Stop-signal reaction time (SSRT)) is a quantitative and heritable measure of interindividual variation in brain function. Although attention has been given to the optimal method of SSRT estimation, and initial evidence exists in support of its reliability, there is still variability in how Stop-signal task data are treated across samples. In order to examine this issue, we pooled data across three separate studies and examined the influence of multiple SSRT calculation methods and outlier calling on reliability (using Intra-class correlation). Our results suggest that an approach which uses the average of all available sessions, all trials of each session, and excludes outliers based on predetermined lenient criteria yields reliable SSRT estimates, while not excluding too many participants. Our findings further support the reliability of SSRT, which is commonly used as an index of inhibitory control, and provide support for its continued use as a neurocognitive phenotype.

Abstract

Functional imaging studies examining the neural correlates of risk have mainly relied on paradigms involving exposure to simple chance gambles and an economic definition of risk as variance in the probability distribution over possible outcomes. However, there is little evidence that choices made during gambling tasks predict naturalistic risk-taking behaviors such as drug use, extreme sports, or even equity investing. To better understand the neural basis of naturalistic risk-taking, we scanned participants using fMRI while they completed the Balloon Analog Risk Task, an experimental measure that includes an active decision/choice component and that has been found to correlate with a number of naturalistic risk-taking behaviors. In the task, as in many naturalistic settings, escalating risk-taking occurs under uncertainty and might be experienced either as the accumulation of greater potential rewards, or as exposure to increasing possible losses (and decreasing expected value). We found that areas previously linked to risk and risk-taking (bilateral anterior insula, anterior cingulate cortex, and right dorsolateral prefrontal cortex) were activated as participants continued to inflate balloons. Interestingly, we found that ventromedial prefrontal cortex (vmPFC) activity decreased as participants further expanded balloons. In light of previous findings implicating the vmPFC in value calculation, this result suggests that escalating risk-taking in the task might be perceived as exposure to increasing possible losses (and decreasing expected value) rather than the increasing potential total reward relative to the starting point of the trial. A better understanding of how neural activity changes with risk-taking behavior in the task offers insight into the potential neural mechanisms driving naturalistic risk-taking.

Abstract

The right inferior frontal gyrus (rIFG) has been hypothesized to mediate response inhibition. Typically response inhibition is signaled by an external stop cue, which provides a top-down signal to initiate the process. However, recent behavioral findings suggest that response inhibition can also be triggered automatically by bottom-up processes. In the present study, we evaluated whether rIFG activity would also be observed during automatic inhibition, in which no stop cue was presented and no motor inhibition was actually required. We measured rIFG activation in response to stimuli that were previously associated with stop signals but which required a response on the current trial (reversal trials). The results revealed an increase in rIFG (pars triangularis) activity, suggesting that it can be activated by associations between stimuli and stopping. Moreover, its role in inhibition tasks is not contingent on the presence of an external stop cue. We conclude that rIFG involvement in stopping is consistent with a role in reprogramming of action plans, which may comprise inhibition, and its activity can be triggered through automatic, bottom-up processing.

Abstract

The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.

Abstract

The development of MRI measures as biomarkers for neurodegenerative disease could prove extremely valuable for the assessment of neuroprotective therapies. Much current research is aimed at developing such biomarkers for use in people who are gene-positive for Huntington's disease yet exhibit few or no clinical symptoms of the disease (pre-HD). We acquired structural (T1), diffusion weighted and functional MRI (fMRI) data from 39 pre-HD volunteers and 25 age-matched controls. To determine whether it was possible to decode information about disease state from neuroimaging data, we applied multivariate pattern analysis techniques to several derived voxel-based and segmented region-based datasets. We found that different measures of structural, diffusion weighted, and functional MRI could successfully classify pre-HD and controls using support vector machines (SVM) and linear discriminant analysis (LDA) with up to 76% accuracy. The model producing the highest classification accuracy used LDA with a set of six volume measures from the basal ganglia. Furthermore, using support vector regression (SVR) and linear regression models, we were able to generate quantitative measures of disease progression that were significantly correlated with established measures of disease progression (estimated years to clinical onset, derived from age and genetic information) from several different neuroimaging measures. The best performing regression models used SVR with neuroimaging data from regions within the grey matter (caudate), white matter (corticospinal tract), and fMRI (insular cortex). These results highlight the utility of machine learning analyses in addition to conventional ones. We have shown that several neuroimaging measures contain multivariate patterns of information that are useful for the development of disease-state biomarkers for HD.

Abstract

Methamphetamine (MA)-dependent individuals exhibit deficits in cognition and prefrontal cortical function. Therefore, medications that improve cognition in these subjects may improve the success of therapy for their addiction, especially when cognitive behavioral therapies are used. Modafinil has been shown to improve cognitive performance in neuropsychiatric patients and healthy volunteers. We therefore conducted a randomized, double-blind, placebo-controlled, cross-over study, using functional magnetic resonance imaging, to examine the effects of modafinil on learning and neural activity related to cognitive function in abstinent, MA-dependent, and healthy control participants. Modafinil (200 mg) and placebo were administered orally (one single dose each), in counterbalanced fashion, 2 h before each of two testing sessions. Under placebo conditions, MA-dependent participants showed worse learning performance than control participants. Modafinil boosted learning in MA-dependent participants, bringing them to the same performance level as control subjects; the control group did not show changes in performance with modafinil. After controlling for performance differences, MA-dependent participants showed a greater effect of modafinil on brain activation in bilateral insula/ventrolateral prefrontal cortex and anterior cingulate cortices than control participants. The findings suggest that modafinil improves learning in MA-dependent participants, possibly by enhancing neural function in regions important for learning and cognitive control. These results suggest that modafinil may be a suitable pharmacological adjunct for enhancing the efficiency of cognitive-based therapies for MA dependence.

Abstract

Smoking is usually initiated in adolescence, and is the leading preventable cause of death in the United States. Little is known, however, about the links between smoking and neurobiological function in adolescent smokers. This study aimed to probe prefrontal cortical function in late adolescent smokers, using a response inhibition task, and to assess possible relationships between inhibition-related brain activity, clinical features of smoking behavior, and exposure to cigarette smoking. Participants in this study were otherwise healthy late adolescent smokers (15-21 years of age; n=25), who reported daily smoking for at least the 6 months before testing, and age- and education-matched nonsmokers (16-21 years of age; n=25), who each reported smoking fewer than five cigarettes in their lifetimes. The subjects performed the Stop-signal Task, while undergoing functional magnetic resonance imaging. There were no significant group differences in prefrontal cortical activity during response inhibition, but the Heaviness of Smoking Index, a measure of smoking behavior and dependence, was negatively related to neural function in cortical regions of the smokers. These findings suggest that smoking can modulate prefrontal cortical function. Given the late development of the prefrontal cortex, which continues through adolescence, it is possible that smoking may influence the trajectory of brain development during this critical developmental period.

Abstract

Psychological and neurocognitive studies have suggested that different kinds of self-control may share a common psychobiological component. If this is true, performance in affective and nonaffective inhibitory control tasks in the same individuals should be correlated and should rely upon integrity of this region. To test this hypothesis, we acquired high-resolution magnetic resonance images from 44 healthy and 43 methamphetamine-dependent subjects. Individuals with methamphetamine dependence were tested because of prior findings that they suffer inhibitory control deficits. Gray matter structure of the inferior frontal gyrus was assessed using voxel-based morphometry. Subjects participated in tests of motor and affective inhibitory control (stop-signal task and emotion reappraisal task, respectively); and methamphetamine-dependent subjects provided self-reports of their craving for methamphetamine. Performance levels on the two inhibitory control tasks were correlated with one another and with gray matter intensity in the right pars opercularis region of the inferior frontal gyrus in healthy subjects. Gray matter intensity of this region was also correlated with methamphetamine craving. Compared with healthy subjects, methamphetamine-dependent subjects exhibited lower gray matter intensity in this region, worse motor inhibitory control, and less success in affect regulation. These findings suggest that self-control in different psychological domains involves a common substrate in the right pars opercularis, and that successful self-control depends on integrity of this substrate.

Abstract

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.

Abstract

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.

Abstract

Genetic studies are rapidly identifying variants that shape risk for disorders of human cognition, but the question of how such variants predispose to neuropsychiatric disease remains. Noninvasive human brain imaging allows assessment of the brain in vivo, and the combination of genetics and imaging phenotypes remains one of the only ways to explore functional genotype-phenotype associations in human brain. Common variants in contactin-associated protein-like 2 (CNTNAP2), a neurexin superfamily member, have been associated with several allied neurodevelopmental disorders, including autism and specific language impairment, and CNTNAP2 is highly expressed in frontal lobe circuits in the developing human brain. Using functional neuroimaging, we have demonstrated a relationship between frontal lobar connectivity and common genetic variants in CNTNAP2. These data provide a mechanistic link between specific genetic risk for neurodevelopmental disorders and empirical data implicating dysfunction of long-range connections within the frontal lobe in autism. The convergence between genetic findings and cognitive-behavioral models of autism provides evidence that genetic variation at CNTNAP2 predisposes to diseases such as autism in part through modulation of frontal lobe connectivity.

Abstract

Cognitive neuroscientists increasingly recognize that continued progress in understanding human brain function will require not only the acquisition of new data, but also the synthesis and integration of data across studies and laboratories. Here we review ongoing efforts to develop a more cumulative science of human brain function. We discuss the rationale for an increased focus on formal synthesis of the cognitive neuroscience literature, provide an overview of recently developed tools and platforms designed to facilitate the sharing and integration of neuroimaging data, and conclude with a discussion of several emerging developments that hold even greater promise in advancing the study of human brain function.

Abstract

Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examining the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of voxelwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent component analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.

Abstract

The goal of cognitive neuroscience is to identify the mapping between brain function and mental processing. In this article, I examine the strategies that have been used to identify such mappings and argue that they may be fundamentally unable to identify selective structure-function mappings. To understand the functional anatomy of mental processes, it will be necessary for researchers to move from the brain-mapping strategies that the field has employed toward a search for selective associations. This will require a greater focus on the structure of cognitive processes, which can be achieved through the development of formal ontologies that describe the structure of mental processes. In this article, I outline the Cognitive Atlas Project, which is developing such ontologies, and show how this knowledge could be used in conjunction with data-mining approaches to more directly relate mental processes and brain function.

Abstract

Elucidating the molecular mechanisms underlying quantitative neurocognitive phenotypes will further our understanding of the brain's structural and functional architecture and advance the diagnosis and treatment of the psychiatric disorders that these traits underlie. Although many neurocognitive traits are highly heritable, little progress has been made in identifying genetic variants unequivocally associated with these phenotypes. A major obstacle to such progress is the difficulty in identifying heritable neurocognitive measures that are precisely defined and systematically assessed and represent unambiguous mental constructs, yet are also amenable to the high-throughput phenotyping necessary to obtain adequate power for genetic association studies. In this perspective we compare the current status of genetic investigations of neurocognitive phenotypes to that of other categories of biomedically relevant traits and suggest strategies for genetically dissecting traits that may underlie disorders of brain and behavior.

Abstract

The left midfusiform and adjacent regions have been implicated in processing and memorizing familiar words, yet its role in memorizing novel characters has not been well understood.Using functional MRI, the present study examined the hypothesis that the left midfusiform is also involved in memorizing novel characters and spaced learning could enhance the memory by enhancing the left midfusiform activity during learning. Nineteen native Chinese readers were scanned while memorizing the visual form of 120 Korean characters that were novel to the subjects. Each character was repeated four times during learning. Repetition suppression was manipulated by using two different repetition schedules: massed learning and spaced learning, pseudo-randomly mixed within the same scanning session. Under the massed learning condition, the four repetitions were consecutive (with a jittered inter-repetition interval to improve the design efficiency). Under the spaced learning condition, the four repetitions were interleaved with a minimal inter-repetition lag of 6 stimuli. Spaced learning significantly improved participants' performance during the recognition memory test administered one hour after the scan. Stronger left midfusiform and inferior temporal gyrus activities during learning (summed across four repetitions) were associated with better memory of the characters, based on both within- and cross-subjects analyses. Compared to massed learning, spaced learning significantly reduced neural repetition suppression and increased the overall activities in these regions, which were associated with better memory for novel characters.These results demonstrated a strong link between cortical activity in the left midfusiform and memory for novel characters, and thus challenge the visual word form area (VWFA) hypothesis. Our results also shed light on the neural mechanisms of the spacing effect in memorizing novel characters.

Abstract

Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results.

Abstract

The goal of cognitive neuroscience is to map mental functions onto their neural substrates. We argue here that this goal requires a formal approach to the characterization of mental processes, and we present one such approach by using ontologies to describe cognitive processes and their relations. Using a classifier analysis of data from the BrainMap database, we examine the concept of "cognitive control" to determine whether the proposed component processes in this domain are mapped to independent neural systems. These results show that some subcomponents can be uniquely classified, whereas others cannot, suggesting that these different components may vary in their ontological reality. We relate these concepts to the broader emerging field of phenomics, which aims to characterize cognitive phenotypes on a global scale.

Abstract

Over the past year, a heated discussion about 'circular' or 'nonindependent' analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in

Abstract

The ability to flexibly respond to changes in the environment is critical for adaptive behavior. Reversal learning (RL) procedures test adaptive response updating when contingencies are altered. We used functional magnetic resonance imaging to examine brain areas that support specific RL components. We compared neural responses to RL and initial learning (acquisition) to isolate reversal-related brain activation independent of cognitive control processes invoked during initial feedback-based learning. Lateral orbitofrontal cortex (OFC) was more activated during reversal than acquisition, suggesting its relevance for reformation of established stimulus-response associations. In addition, the dorsal anterior cingulate (dACC) and right inferior frontal gyrus (rIFG) correlated with change in postreversal accuracy. Because optimal RL likely requires suppression of a prior learned response, we hypothesized that similar regions serve both response inhibition (RI) and inhibition of learned associations during reversal. However, reversal-specific responding and stopping (requiring RI and assessed via the stop-signal task) revealed distinct frontal regions. Although RI-related regions do not appear to support inhibition of prepotent learned associations, a subset of these regions, dACC and rIFG, guide actions consistent with current reward contingencies. These regions and lateral OFC represent distinct neural components that support behavioral flexibility important for adaptive learning.

Abstract

Response inhibition is thought to improve throughout childhood and into adulthood. Despite the relationship between age and the ability to stop ongoing behavior, questions remain regarding whether these age-related changes reflect improvements in response inhibition or in other factors that contribute to response performance variability. Functional neuroimaging data shows age-related changes in neural activity during response inhibition. While traditional methods of exploring neuroimaging data are limited to determining correlational relationships, newer methods can determine predictability and can begin to answer these questions. Therefore, the goal of the current study was to determine which aspects of neural function predict individual differences in age, inhibitory function, response speed, and response time variability. We administered a stop-signal task requiring rapid inhibition of ongoing motor responses to healthy participants aged 9-30. We conducted a standard analysis using GLM and a predictive analysis using high-dimensional regression methods. During successful response inhibition we found regions typically involved in motor control, such as the ACC and striatum, that were correlated with either age, response inhibition (as indexed by stop-signal reaction time; SSRT), response speed, or response time variability. However, when examining which variables neural data could predict, we found that age and SSRT, but not speed or variability of response execution, were predicted by neural activity during successful response inhibition. This predictive relationship provides novel evidence that developmental differences and individual differences in response inhibition are related specifically to inhibitory processes. More generally, this study demonstrates a new approach to identifying the neurocognitive bases of individual differences.

Abstract

While much is known about the neural regions recruited in the human brain when a dominant motor response becomes inappropriate and must be stopped, less is known about the regions that support switching to a new, appropriate, response. Using functional magnetic resonance imaging with two variants of the stop-signal paradigm that require either stopping altogether or switching to a different response, we examined the brain systems involved in these two forms of executive control. Both stopping trials and switching trials showed common recruitment of the right inferior frontal gyrus, presupplementary motor area, and midbrain. Contrasting switching trials with stopping trials showed activation similar to that observed on response trials (where the initial response remains appropriate and no control is invoked), whereas there were no regions that showed significantly greater activity for stopping trials compared with switching trials. These results show that response switching can be supported by the same neural systems as response inhibition, and suggest that the same mechanism of rapid, nonselective response inhibition that is thought to support speeded response stopping can also support speeded response switching when paired with execution of the new, appropriate, response.

Abstract

The imaging of developmental changes in brain function is challenging, but great strides have been made in addressing many of the conceptual issues that this work raises. I highlight a set of issues that remain to be addressed in this literature. First, I argue that the appeal to developmental neurobiology is often misplaced, as it focuses on neurodevelopmental processes that are mostly completed by the age at which neuroimaging studies can be performed. Second, I argue that the concept of "normative" development needs to be reexamined, as it reflects fundamental value judgments about brain development that seem inappropriate for scientific investigation. Third, I examine the ways in which developmental changes are often interpreted, arguing that common interpretations, including the concepts of "efficiency" and "focalization" may be less useful than commonly supposed. To put developmental neuroimaging on stronger footing, we need to develop stronger connections between computational and neurobiological accounts of developmental changes.

Abstract

The social motivation hypothesis of autism posits that infants with autism do not experience social stimuli as rewarding, thereby leading to a cascade of potentially negative consequences for later development. While possible downstream effects of this hypothesis such as altered face and voice processing have been examined, there has not been a direct investigation of social reward processing in autism. Here we use functional magnetic resonance imaging to examine social and monetary rewarded implicit learning in children with and without autism spectrum disorders (ASD). Sixteen males with ASD and sixteen age- and IQ-matched typically developing (TD) males were scanned while performing two versions of a rewarded implicit learning task. In addition to examining responses to reward, we investigated the neural circuitry supporting rewarded learning and the relationship between these factors and social development. We found diminished neural responses to both social and monetary rewards in ASD, with a pronounced reduction in response to social rewards (SR). Children with ASD also demonstrated a further deficit in frontostriatal response during social, but not monetary, rewarded learning. Moreover, we show a relationship between ventral striatum activity and social reciprocity in TD children. Together, these data support the hypothesis that children with ASD have diminished neural responses to SR, and that this deficit relates to social learning impairments.

Abstract

The ability to draw analogies requires 2 key cognitive processes, relational integration and resolution of interference. The present study aimed to identify the neural correlates of both component processes of analogical reasoning within a single, nonverbal analogy task using event-related functional magnetic resonance imaging. Participants verified whether a visual analogy was true by considering either 1 or 3 relational dimensions. On half of the trials, there was an additional need to resolve interference in order to make a correct judgment. Increase in the number of dimensions to integrate was associated with increased activation in the lateral prefrontal cortex as well as lateral frontal pole in both hemispheres. When there was a need to resolve interference during reasoning, activation increased in the lateral prefrontal cortex but not in the frontal pole. We identified regions in the middle and inferior frontal gyri which were exclusively sensitive to demands on each component process, in addition to a partial overlap between these neural correlates of each component process. These results indicate that analogical reasoning is mediated by the coordination of multiple regions of the prefrontal cortex, of which some are sensitive to demands on only one of these 2 component processes, whereas others are sensitive to both.

Abstract

Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.

Abstract

While methamphetamine addiction has been associated with both impulsivity and striatal dopamine D(2)/D(3) receptor deficits, human studies have not directly linked the latter two entities. We therefore compared methamphetamine-dependent and healthy control subjects using the Barratt Impulsiveness Scale (version 11, BIS-11) and positron emission tomography with [(18)F]fallypride to measure striatal dopamine D(2)/D(3) receptor availability. The methamphetamine-dependent subjects reported recent use of the drug 3.3 g per week, and a history of using methamphetamine, on average, for 12.5 years. They had higher scores than healthy control subjects on all BIS-11 impulsiveness subscales (p < 0.001). Volume-of-interest analysis found lower striatal D(2)/D(3) receptor availability in methamphetamine-dependent than in healthy control subjects (p < 0.01) and a negative relationship between impulsiveness and striatal D(2)/D(3) receptor availability in the caudate nucleus and nucleus accumbens that reached statistical significance in methamphetamine-dependent subjects. Combining data from both groups, voxelwise analysis indicated that impulsiveness was related to D(2)/D(3) receptor availability in left caudate nucleus and right lateral putamen/claustrum (p < 0.05, determined by threshold-free cluster enhancement). In separate group analyses, correlations involving the head and body of the caudate and the putamen of methamphetamine-dependent subjects and the lateral putamen/claustrum of control subjects were observed at a weaker threshold (p < 0.12 corrected). The findings suggest that low striatal D(2)/D(3) receptor availability may mediate impulsive temperament and thereby influence addiction.

Abstract

Phenomics is an emerging transdiscipline dedicated to the systematic study of phenotypes on a genome-wide scale. New methods for high-throughput genotyping have changed the priority for biomedical research to phenotyping, but the human phenome is vast and its dimensionality remains unknown. Phenomics research strategies capable of linking genetic variation to public health concerns need to prioritize development of mechanistic frameworks that relate neural systems functioning to human behavior. New approaches to phenotype definition will benefit from crossing neuropsychiatric syndromal boundaries, and defining phenotypic features across multiple levels of expression from proteome to syndrome. The demand for high throughput phenotyping may stimulate a migration from conventional laboratory to web-based assessment of behavior, and this offers the promise of dynamic phenotyping-the iterative refinement of phenotype assays based on prior genotype-phenotype associations. Phenotypes that can be studied across species may provide greatest traction, particularly given rapid development in transgenic modeling. Phenomics research demands vertically integrated research teams, novel analytic strategies and informatics infrastructure to help manage complexity. The Consortium for Neuropsychiatric Phenomics at UCLA has been supported by the National Institutes of Health Roadmap Initiative to illustrate these principles, and is developing applications that may help investigators assemble, visualize, and ultimately test multi-level phenomics hypotheses. As the transdiscipline of phenomics matures, and work is extended to large-scale international collaborations, there is promise that systematic new knowledge bases will help fulfill the promise of personalized medicine and the rational diagnosis and treatment of neuropsychiatric syndromes.

Abstract

Refining phenotypes for the study of neuropsychiatric disorders is of paramount importance in neuroscience. Poor phenotype definition provides the greatest obstacle for making progress in disorders like schizophrenia, bipolar disorder, Attention Deficit/Hyperactivity Disorder (ADHD), and autism. Using freely available informatics tools developed by the Consortium for Neuropsychiatric Phenomics (CNP), we provide a framework for defining and refining latent constructs used in neuroscience research and then apply this strategy to review known genetic contributions to memory and intelligence in healthy individuals. This approach can help us begin to build multi-level phenotype models that express the interactions between constructs necessary to understand complex neuropsychiatric diseases. These results are available online through the http://www.phenowiki.org database. Further work needs to be done in order to provide consensus-building applications for the broadly defined constructs used in neuroscience research.

Abstract

Since the observation of the blood oxygenation level dependent (BOLD) effect on measured MR signal in the brain, functional magnetic resonance imaging (fMRI) has rapidly become the tool of choice for exploring brain function in cognitive neuroscience. Although fMRI is an exciting and powerful means to examining the brain in vivo, the field has sometimes permitted itself to believe that patterns of BOLD activity reveal more than it is possible to measure given the method's spatial and temporal sampling, while concurrently not fully exploring the amount of information it provides. In this article, we examine some of the constraints on the kinds of inferences that can be supported by fMRI. We critique the concept of reverse inference that is often employed to claim some cognitive function must be present given activity in a specific region. We review the consideration of functional and effective connectivity that remain infrequently applied in cognitive neuroimaging, highlighting recent thinking on the ways in which functional imaging can be used to characterize inter-regional communication. Recent advances in neuroimaging that make it possible to assess anatomical connectivity using diffusion tensor imaging (DTI) and we discuss how these may inform interpretation of fMRI results. Descriptions of fMRI studies in the media, in some instances, serve to misrepresent fMRI's capabilities. We comment on how researchers need to faithfully represent fMRI's promise and limitations in dealing with the media. Finally, as we stand at the crossroads of fMRI research, where one pathway leads toward a rigorous understanding of cognitive operations using fMRI and another leads us to a predictable collection of observations absent of clear insight, we offer our impressions of a fruitful path for future functional imaging research.

Abstract

We discuss the effects of non-independence on region of interest (ROI) analysis of functional magnetic resonance imaging data, which has recently been raised in a prominent article by Vul et al. We outline the problem of non-independence, and use a previously published dataset to examine the effects of non-independence. These analyses show that very strong correlations (exceeding 0.8) can occur even when the ROI is completely independent of the data being analyzed, suggesting that the claims of Vul et al. regarding the implausibility of these high correlations are incorrect. We conclude with some recommendations to help limit the potential problems caused by non-independence.

Abstract

The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) was focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of executive control, the 2 constructs of interest were "rule generation and selection" and "dynamic adjustments in control." CNTRICS received 4 task nominations for each of these constructs, and the breakout group for executive control evaluated the degree to which each of these tasks met prespecified criteria. For rule generation and selection, the breakout group for executive control recommended the intradimensional/extradimensional shift task and the switching Stroop for translation for use in clinical trial contexts in schizophrenia research. For dynamic adjustments in control, the breakout group recommended conflict and error adaptation in the Stroop and the stop signal task for translation for use in clinical trials. This article describes the ways in which each of these tasks met the criteria used by the breakout group to recommend tasks for further development.

Abstract

Now that genome-wide association studies (GWAS) are dominating the landscape of genetic research on neuropsychiatric syndromes, investigators are being faced with complexity on an unprecedented scale. It is now clear that phenomics, the systematic study of phenotypes on a genome-wide scale, comprises a rate-limiting step on the road to genomic discovery. To gain traction on the myriad paths leading from genomic variation to syndromal manifestations, informatics strategies must be deployed to navigate increasingly broad domains of knowledge and help researchers find the most important signals. The success of the Gene Ontology project suggests the potential benefits of developing schemata to represent higher levels of phenotypic expression. Challenges in cognitive ontology development include the lack of formal definitions of key concepts and relations among entities, the inconsistent use of terminology across investigators and time, and the fact that relations among cognitive concepts are not likely to be well represented by simple hierarchical "tree" structures. Because cognitive concept labels are labile, there is a need to represent empirical findings at the cognitive test indicator level. This level of description has greater consistency, and benefits from operational definitions of its concepts and relations to quantitative data. Considering cognitive test indicators as the foundation of cognitive ontologies carries several implications, including the likely utility of cognitive task taxonomies. The concept of cognitive "test speciation" is introduced to mark the evolution of paradigms sufficiently unique that their results cannot be "mated" productively with others in meta-analysis. Several projects have been initiated to develop cognitive ontologies at the Consortium for Neuropsychiatric Phenomics (www.phenomics.ucla.edu), in the hope that these ultimately will enable more effective collaboration, and facilitate connections of information about cognitive phenotypes to other levels of biological knowledge. Several free web applications are available already to support examination and visualisation of cognitive concepts in the literature (PubGraph, PubAtlas, PubBrain) and to aid collaborative development of cognitive ontologies (Phenowiki and the Cognitive Atlas). It is hoped that these tools will help formalise inference about cognitive concepts in behavioural and neuroimaging studies, and facilitate discovery of the genetic bases of both healthy cognition and cognitive disorders.

Abstract

Adaptive goal-directed actions require the ability to quickly relearn behaviors in a changing environment, yet how the brain supports this ability is barely understood. Using functional magnetic resonance imaging and a novel reversal learning paradigm, the present study examined the neural mechanisms associated with reversal learning for outcomes versus motor responses. Participants were extensively trained to classify novel visual symbols (Japanese Hiraganas) into two arbitrary classes ("male" or "female"), in which subjects could acquire both stimulus-outcome associations and stimulus-response associations. They were then required to relearn either the outcome or the motor response associated with the symbols, or both. The results revealed that during reversal learning, a network including anterior cingulate, posterior inferior frontal, and parietal regions showed extended activation for all types of reversal trials, whereas their activation decreased quickly for trials not involving reversal, suggesting their role in domain-general interference resolution. The later increase of right ventral lateral prefrontal cortex and caudate for reversal of stimulus-outcome associations suggests their importance in outcome reversal learning in the face of interference.

Abstract

The inhibition of speech acts is a critical aspect of human executive control over thought and action, but its neural underpinnings are poorly understood. Using functional magnetic resonance imaging and the stop-signal paradigm, we examined the neural correlates of speech control in comparison to manual motor control. Initiation of a verbal response activated left inferior frontal cortex (IFC: Broca's area). Successful inhibition of speech (naming of letters or pseudowords) engaged a region of right IFC (including pars opercularis and anterior insular cortex) as well as presupplementary motor area (pre-SMA); these regions were also activated by successful inhibition of a hand response (i.e., a button press). Moreover, the speed with which subjects inhibited their responses, stop-signal reaction time, was significantly correlated between speech and manual inhibition tasks. These findings suggest a functional dissociation of left and right IFC in initiating versus inhibiting vocal responses, and that manual responses and speech acts share a common inhibitory mechanism localized in the right IFC and pre-SMA.

The role of fMRI in Cognitive Neuroscience: where do we stand?CURRENT OPINION IN NEUROBIOLOGYPoldrack, R. A.2008; 18 (2): 223-226

Abstract

Functional magnetic resonance imaging (fMRI) has quickly become the most prominent tool in cognitive neuroscience. In this article, I outline some of the limits on the kinds of inferences that can be supported by fMRI, focusing particularly on reverse inference, in which the engagement of specific mental processes is inferred from patterns of brain activation. Although this form of inference is weak, newly developed methods from the field of machine learning offer the potential to formalize and strengthen reverse inferences. I conclude by discussing the increasing presence of fMRI results in the popular media and the ethical implications of the increasing predictive power of fMRI.

Abstract

Structural and functional abnormalities in frontal-parietal circuitry are thought to be associated with working memory (WM) deficits in patients with schizophrenia. This study examines whether recent-onset schizophrenia is associated with anatomical changes in the superior longitudinal fasciculus (SLF), the main frontal-parietal white matter connection, and whether the integrity of the SLF is related to WM performance.We applied a novel registration approach (Tract-Based Spatial Statistics [TBSS]) to diffusion tensor imaging data to examine fractional anisotropy (FA) in the left and right SLF in 12 young adult patients with recent-onset schizophrenia and 17 matched control subjects.Schizophrenia patients showed lower FA values than control subjects across the entire SLF, with particular deficits on the left SLF. Fractional anisotropy values were correlated with performance on a verbal WM task in both patient and control groups in the left but not right SLF.Recent-onset schizophrenia patients show deficits in frontal-parietal connections, key components of WM circuitry. Moreover, the integrity of this physiological connection predicted performance on a verbal WM task, indicating that this structural change may have important functional implications. These findings support the view that schizophrenia is a disorder of brain connectivity and implicate white matter changes detectable in the early phases of the illness as one source of this dysfunction.

Abstract

We examined the relationship between automaticity and response inhibition in the serial reaction time (SRT) task to test the common assertion that automatic behavior is ballistic. Participants trained for 3 h on the SRT, using blocks of a second-order conditional sequence interleaved with random blocks. Automaticity was measured using a concurrent secondary letter-counting task. Response inhibition was measured using a stop-signal task. RTs decreased with training, with agreater decrease for sequenced versusrandom blocks. Training correlated with a decreased RT cost to performing the secondary task concurrently with the SRT, indicating the development of automaticity. Crucially, there was no change in the ability to inhibit responses at the end of training, even in individuals who showed no dual-task interference. These results demonstrate that the ability to inhibit a motor response does not decrease with automaticity, suggesting that some aspects of automatic behavior are not ballistic.

Abstract

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman-Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0.2 and 0.3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.

Abstract

We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.

Abstract

A substantial and growing body of evidence from cognitive neuroscience supports the concept of multiple memory systems (MMS). However, the existence of multiple systems has been questioned by theorists who instead propose that dissociations can be accounted for within a single memory system. We present convergent evidence from neuroimaging and neuropsychological studies of category learning in favor of the existence of MMS for category learning and declarative knowledge. Whereas single-system theorists have argued that their approach is more parsimonious because it only postulates a single form of memory representation, we show that the MMS approach is superior in its ability to account for a broad range of data from psychology and neuroscience.

Abstract

It has been suggested that patients with schizophrenia have corticostriatal circuit dysfunction (Carlsson & Carlsson, 1990). Skill learning is thought to rely on corticostriatal circuitry and different types of skill learning may be related to separable corticostriatal loops (Grafton, Hazeltine, & Ivry, 1995; Poldrack, Prabhakaran, Seger, & Gabrieli, 1999). The authors examined motor (Serial Reaction Time task, SRT) and cognitive (Probabilistic Classification task, PCT) skill learning in patients with schizophrenia and normal controls. Development of automaticity was examined, using a dual task paradigm, across three training sessions. Patients with schizophrenia were impaired at learning on the PCT compared to controls. Performance gains of controls occurred within the first session, whereas patients only improved gradually and never reached the performance level of controls. In contrast, patients were not impaired at learning on the SRT relative to controls, suggesting that patients with schizophrenia may have dysfunction in a specific corticostriatal subcircuit.

Abstract

Abstract It remains under debate whether the fusiform visual word form area (VWFA) is specific to visual word form and whether visual expertise increases its sensitivity (Xue et al., 2006; Cohen et al., 2002). The present study examined three related issues: (1) whether the VWFA is also involved in processing foreign writing that significantly differs from the native one, (2) the effect of visual word form training on VWFA activation after controlling the task difficulty, and (3) the transfer of visual word form learning. Eleven native English speakers were trained, during five sessions, to judge whether two subsequently flashed (100-msec duration with 200-msec interval) foreign characters (i.e., Korean Hangul) were identical or not. Visual noise was added to the stimuli to manipulate task difficulty. In functional magnetic resonance imaging scans before and after training, subjects performed the task once with the same noise level (i.e., parameter-matched scan) and once with noise level changed to match performance from pretraining to posttraining (i.e., performance-matched scan). Results indicated that training increased the accuracy in parameter-matched condition but remained constant in performance-matched condition (because of increasing task difficulty). Pretraining scans revealed stronger activation for English words than for Korean characters in the left inferior temporal gyrus and the left inferior frontal cortex, but not in the VWFA. Visual word form training significantly decreased the activation in the bilateral middle and left posterior fusiform when either parameters or performance were matched and for both trained and new items. These results confirm our conjecture that the VWFA is not dedicated to words, and visual expertise acquired with training reduces rather than increases its activity.

Abstract

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localised to neuroanatomy and suggest that the gold standard should be localisation to the individual's or group's own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localisation may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localisation including a common "standard" space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann's areas.

Abstract

The analysis of group fMRI data requires a statistical model known as the mixed effects model. This article motivates the need for a mixed effects model and outlines the different stages of the mixed model used to analyze group fMRI data. Different modeling options and their impact on analysis results are also described.

Abstract

Probabilistic classification learning can be supported by implicit knowledge of cue-response associations. We investigated whether forming these associations depends on attention by assessing the effect of performing a secondary task on learning in the probabilistic classification task (PCT). Experiment I showed that concurrent task performance significantly interfered with performance of the PCT. Experiment 2 showed that this interference did not prevent learning from occurring. On the other hand, the secondary task did disrupt acquisition of explicit knowledge about cue-outcome associations. These results show that concurrent task performance can have different effects on implicit and explicit knowledge acquired within the same task and also underscore the importance of considering effects on learning and performance separately.

Abstract

The ability to stop motor responses depends critically on the right inferior frontal cortex (IFC) and also engages a midbrain region consistent with the subthalamic nucleus (STN). Here we used diffusion-weighted imaging (DWI) tractography to show that the IFC and the STN region are connected via a white matter tract, which could underlie a "hyperdirect" pathway for basal ganglia control. Using a novel method of "triangulation" analysis of tractography data, we also found that both the IFC and the STN region are connected with the presupplementary motor area (preSMA). We hypothesized that the preSMA could play a conflict detection/resolution role within a network between the preSMA, the IFC, and the STN region. A second experiment tested this idea with functional magnetic resonance imaging (fMRI) using a conditional stop-signal paradigm, enabling examination of behavioral and neural signatures of conflict-induced slowing. The preSMA, IFC, and STN region were significantly activated the greater the conflict-induced slowing. Activation corresponded strongly with spatial foci predicted by the DWI tract analysis, as well as with foci activated by complete response inhibition. The results illustrate how tractography can reveal connections that are verifiable with fMRI. The results also demonstrate a three-way functional-anatomical network in the right hemisphere that could either brake or completely stop responses.

Abstract

A common approach to the analysis of fMRI data involves the extraction of signal from specified regions of interest (or ROI's). Three approaches to ROI analysis are described, and the strengths and assumptions of each method are outlined.

Abstract

First-degree relatives of persons with schizophrenia are at elevated risk for the illness, demonstrate deficits in verbal memory, and exhibit structural abnormalities in the medial temporal lobe (MTL). We used functional magnetic resonance imaging (fMRI) to assess brain activity in the MTL during novel and repeated word-pair encoding.Participants were 21 non-psychotic, first-degree relatives of persons with schizophrenia and 26 matched healthy controls (ages 13-28). fMRI signal change was measured using a Siemens 1.5T MR scanner, and data were analyzed using SPM-2. Verbal memory was assessed using the Miller Selfridge (MS) Context Memory test prior to scanning.The groups were comparable on demographics, intelligence and post-scan word recognition. Relatives at genetic risk (GR) had significantly more psychopathology than controls and worse performance on the MS test (p < .05). GR participants exhibited greater repetition suppression of activation in the left and right anterior parahippocampus (PHA, in the region of the entorhinal cortex region), after controlling for possible confounders. Controls and GR participants with above-median MS performance showed significantly greater repetition suppression of activation in left inferior frontal gyrus than those scoring below the median.This is the first study to demonstrate an alteration of brain activity in the PHA in persons at GR for schizophrenia.

Abstract

People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.

Modulation of competing memory systems by distractionPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICAFoerde, K., Knowlton, B. J., Poldrack, R. A.2006; 103 (31): 11778-11783

Abstract

Different forms of learning and memory depend on functionally and anatomically separable neural circuits [Squire, L. R. (1992) Psychol. Rev. 99, 195-231]. Declarative memory relies on a medial temporal lobe system, whereas habit learning relies on the striatum [Cohen, N. J. & Eichenbaum, H. (1993) Memory, Amnesia, and the Hippocampal System (MIT Press, Cambridge, MA)]. How these systems are engaged to optimize learning and behavior is not clear. Here, we present results from functional neuroimaging showing that the presence of a demanding secondary task during learning modulates the degree to which subjects solve a problem using either declarative memory or habit learning. Dual-task conditions did not reduce accuracy but reduced the amount of declarative learning about the task. Medial temporal lobe activity was correlated with task performance and declarative knowledge after learning under single-task conditions, whereas performance was correlated with striatal activity after dual-task learning conditions. These results demonstrate a fundamental difference in these memory systems in their sensitivity to concurrent distraction. The results are consistent with the notion that declarative and habit learning compete to mediate task performance, and they suggest that the presence of distraction can bias this competition. These results have implications for learning in multitask situations, suggesting that, even if distraction does not decrease the overall level of learning, it can result in the acquisition of knowledge that can be applied less flexibly in new situations.

Abstract

Adult first-degree relatives of persons with schizophrenia carry elevated genetic risk for the illness, demonstrate working memory (WM) impairments, and manifest alterations in dorsolateral prefrontal cortical (DLPFC) function during WM. Because substantially less is known about these phenotypes in adolescent subjects we sought to demonstrate that young relatives of persons with schizophrenia manifest impaired WM and altered prefrontal activation.Participants were 21 non-psychotic, unmedicated first-degree relatives of persons with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder, depressed type and 24 unmedicated controls, recruited from the community and hospitals in metropolitan Boston (ages 13-28). We compared groups on an auditory WM task with interference prior to scanning and used functional magnetic resonance imaging (fMRI) to compare groups while performing visual 2-back WM and control vigilance tasks. Blood oxygen level dependent signal change was measured using two whole-brain gradient echo EPI pulse acquisitions (21 contiguous, 5mm axial slices), acquired on a Siemens 1.5T MR scanner. Data were analyzed using Statistical Parametric Mapping-99.The high risk subjects were significantly impaired on the auditory WM task, had significantly greater Phobic Anxiety, and marginally greater Psychoticism than controls on the Symptom Checklist-90-Revised, and showed significantly greater task-elicited activation in the right DLPFC (BA 46). Psychopathology, IQ, and in-scanner WM performance did not account for group differences in brain activation.Data support a physiological difference (an exaggerated fMRI response) in DLPFC in adolescents at genetic risk for schizophrenia, independent of psychosis. Future work can study the relationship of these measures to possible onset of schizophrenia.

Abstract

A prominent theory in neuroscience suggests reward learning is driven by the discrepancy between a subject's expectation of an outcome and the actual outcome itself. Furthermore, it is postulated that midbrain dopamine neurons relay this mismatch to target regions including the ventral striatum. Using functional MRI (fMRI), we tested striatal responses to prediction errors for probabilistic classification learning with purely cognitive feedback. We used a version of the Rescorla-Wagner model to generate prediction errors for each subject and then entered these in a parametric analysis of fMRI activity. Activation in ventral striatum/nucleus-accumbens (Nacc) increased parametrically with prediction error for negative feedback. This result extends recent neuroimaging findings in reward learning by showing that learning with cognitive feedback also depends on the same circuitry and dopaminergic signaling mechanisms.

Abstract

Suppressing an already initiated manual response depends critically on the right inferior frontal cortex (IFC), yet it is unclear how this inhibitory function is implemented in the motor system. It has been suggested that the subthalamic nucleus (STN), which is a part of the basal ganglia, may play a role because it is well placed to suppress the "direct" fronto-striatal pathway that is activated by response initiation. In two experiments, we investigated this hypothesis with functional magnetic resonance imaging and a Stop-signal task. Subjects responded to Go signals and attempted to inhibit the initiated response to occasional Stop signals. In experiment 1, Going significantly activated frontal, striatal, pallidal, and motor cortical regions, consistent with the direct pathway, whereas Stopping significantly activated right IFC and STN. In addition, Stopping-related activation was significantly greater for fast inhibitors than slow ones in both IFC and STN, and activity in these regions was correlated across subjects. In experiment 2, high-resolution functional and structural imaging confirmed the location of Stopping activation within the vicinity of the STN. We propose that the role of the STN is to suppress thalamocortical output, thereby blocking Go response execution. These results provide convergent data for a role for the STN in Stop-signal response inhibition. They also suggest that the speed of Go and Stop processes could relate to the relative activation of different neural pathways. Future research is required to establish whether Stop-signal inhibition could be implemented via a direct functional neuroanatomic projection between IFC and STN (a "hyperdirect" pathway).

Abstract

Functional MRI is widely used for imaging the neural correlates of psychological processes and how these brain processes change with learning, development and neuropsychiatric disorder. In order to interpret changes in imaging signals over time, for example, in patient studies, the long-term reliability of fMRI must first be established. Here, eight healthy adult subjects were scanned on two sessions, 1 year apart, while performing a classification learning task known to activate frontostriatal circuitry. We show that behavioral performance and frontostriatal activation were highly concordant at a group level at both time-points. Furthermore, intra-class correlation coefficients (ICCs), which index the degree of correlation between subjects at different time-points, were high for behavior and for functional activation. ICC was significantly higher within the network recruited by learning than outside that network. We conclude that fMRI can have high long-term test-retest reliability, making it suitable as a biomarker for brain development and neurodegeneration.

Abstract

There is much interest currently in using functional neuroimaging techniques to understand better the nature of cognition. One particular practice that has become common is 'reverse inference', by which the engagement of a particular cognitive process is inferred from the activation of a particular brain region. Such inferences are not deductively valid, but can still provide some information. Using a Bayesian analysis of the BrainMap neuroimaging database, I characterize the amount of additional evidence in favor of the engagement of a cognitive process that can be offered by a reverse inference. Its usefulness is particularly limited by the selectivity of activation in the region of interest. I argue that cognitive neuroscientists should be circumspect in the use of reverse inference, particularly when selectivity of the region in question cannot be established or is known to be weak.

Abstract

Sex-specific behaviors are in part based on hormonal regulation of brain physiology. This functional magnetic resonance imaging (fMRI) study demonstrated significant differences in activation of hypothalamic-pituitary-adrenal (HPA) circuitry in adult women with attenuation during ovulation and increased activation during early follicular phase. Twelve normal premenopausal women were scanned twice during the early follicular menstrual cycle phase compared with late follicular/midcycle, using negative valence/high arousal versus neutral visual stimuli, validated by concomitant electrodermal activity (EDA). Significantly greater magnitude of blood oxygenation level-dependent signal changes were found during early follicular compared with midcycle timing in central amygdala, paraventricular and ventromedial hypothalamic nuclei, hippocampus, orbitofrontal cortex (OFC), anterior cingulate gyrus (aCING), and peripeduncular nucleus of the brainstem, a network of regions implicated in the stress response. Arousal (EDA) correlated positively with brain activity in amygdala, OFC, and aCING during midcycle but not in early follicular, suggesting less cortical control of amygdala during early follicular, when arousal was increased. This is the first evidence suggesting that estrogen may likely attenuate arousal in women via cortical-subcortical control within HPA circuitry. Findings have important implications for normal sex-specific physiological functioning and may contribute to understanding higher rates of mood and anxiety disorders in women and differential sensitivity to trauma than men.

Abstract

Acquisition of phonological processing skills, such as the ability to segment words into corresponding speech sounds, is critical to the development of efficient reading. Prior neuroimaging studies of phonological processing have often relied on auditory stimuli or print-mediated tasks that may be problematic for various theoretical and empirical reasons. For the current study, we developed a task to evaluate phonological processing that used visual stimuli but did not require interpretation of orthographic forms. This task requires the subject to retrieve the names of objects and to compare their first sounds; then, the subject must indicate if the initial sounds of the names of the pictures are the same. The phonological analysis task was compared to both a baseline matching task and a more complex control condition in which the participants evaluated two different pictures and indicated whether they represented the same object. The complex picture-matching condition controls for the visual complexity of the stimuli but does not require phonological analysis of the names of the objects. While both frontal and ventral posterior areas were activated in response to phonological analysis of the names of pictures, only inferior and superior frontal gyrus exhibited differential sensitivity to the phonological comparison task as compared to the complex picture-matching control task. These findings suggest that phonological processing that is not mediated by print relies primarily on frontal language processing areas among skilled readers.

Abstract

Functional imaging studies of sex effects in working memory (WMEM) are few, despite significant normal sex differences in brain regions implicated in WMEM. This functional MRI (fMRI) study tested for sex effects in an auditory verbal WMEM task in prefrontal, parietal, cingulate, and insula regions. Fourteen healthy, right-handed community subjects were comparable between the sexes, including on WMEM performance. Per statistical parametric mapping, women exhibited greater signal intensity changes in middle, inferior, and orbital prefrontal cortices than men (corrected for multiple comparisons). A test of mixed-sex groups, comparable on performance, showed no significant differences in the hypothesized regions, providing evidence for discriminant validity for significant sex differences. The findings suggest that combining men and women in fMRI studies of cognition may obscure or bias results.

Abstract

Psychological functions that are behaviorally and neurally well specified may serve as endophenotypes for attention-deficit/hyperactivity disorder (ADHD) research. Such endophenotypes, which lie between genes and symptoms, may relate more directly to relevant genetic variability than does the clinical ADHD syndrome itself. Here we review evidence in favor of response inhibition as an endophenotype for ADHD research. We show that response inhibition--operationalized by Go/NoGo or Stop-signal tasks--requires the prefrontal cortex (PFC), in particular the right inferior frontal cortex (IFC); that patients with ADHD have significant response inhibition deficits and show altered functional activation and gray matter volumes in right IFC; and that a number of studies indicate that response inhibition performance is heritable. Additionally, we review evidence concerning the role of the basal ganglia in response inhibition, as well as the role of neuromodulatory systems. All things considered, a combined right IFC structure/function/response inhibition phenotype is a particularly good candidate for future heritability and association studies. Moreover, a dissection of response inhibition into more basic components such as rule maintenance, vigilance, and target detection may provide yet better targets for association with genes for neuromodulation and brain development.

Abstract

Acquisition of a new skill is generally associated with a decrease in the need for effortful control over performance, leading to the development of automaticity. Automaticity by definition has been achieved when performance of a primary task is minimally affected by other ongoing tasks. The neural basis of automaticity was examined by testing subjects in a serial reaction time (SRT) task under both single-task and dual-task conditions. The diminishing cost of dual-task performance was used as an index for automaticity. Subjects performed the SRT task during two functional magnetic imaging sessions separated by 3 h of behavioral training over multiple days. Behavioral data showed that, by the end of testing, subjects had automated performance of the SRT task. Before behavioral training, performance of the SRT task concurrently with the secondary task elicited activation in a wide network of frontal and striatal regions, as well as parietal lobe. After extensive behavioral training, dual-task performance showed comparatively less activity in bilateral ventral premotor regions, right middle frontal gyrus, and right caudate body; activity in other prefrontal and striatal regions decreased equally for single-task and dual-task conditions. These data suggest that lateral and dorsolateral prefrontal regions, and their corresponding striatal targets, subserve the executive processes involved in novice dual-task performance. The results also showed that supplementary motor area and putamen/globus pallidus regions showed training-related decreases for sequence conditions but not for random conditions, confirming the role of these regions in the representation of learned motor sequences.

Abstract

Studies of prefrontal cortical (PFC) function in schizophrenia have been inconsistent, with studies showing both increased and decreased PFC activation compared to healthy controls. Discrepant findings may be due to task performance effects or demographic differences between samples. We report functional magnetic resonance imaging (fMRI) data comparing subjects with schizophrenia and healthy controls performing a 2-back working memory (WM) task, addressing the effects of task performance.Twenty-two controls and 14 patients with DSM-IV schizophrenia, scanned on a Siemens 1.5 T scanner, performed a visual letter 2-back task and control task (CPT-X) during fMRI. Data were analyzed using Statistical Parametric Mapping (SPM)-99.After statistical adjustment for performance differences, persons with schizophrenia showed significantly greater activation than controls in the right medial frontal gyrus and left inferior parietal lobule/medial temporal gyrus region (BA 39/40), and a trend toward greater activation in the left ventrolateral PFC. This pattern was also observed in demographically matched subgroups of participants.Data are consistent with findings reported in recent studies showing increased PFC and parietal activation in schizophrenia when the effects of reduced WM task performance in patients with schizophrenia are addressed. Further studies are needed to clarify the pathophysiological basis of WM load sensitivity in schizophrenia and its relationship to genes.

Abstract

Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263-291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297-323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making.

Abstract

Attention-deficit/hyperactivity disorder (ADHD) in adults is an increasingly recognized psychiatric disorder, linked with impairments in numerous life domains and with neurocognitive dysfunctions. However, the neural substrate of cognitive functioning in adults with this disorder has been relatively unexamined. The objective of this study was to examine neural functioning in ADHD adults during performance on a verbal working memory task.A sample of unmedicated adults with ADHD (n = 20) and control subjects (n = 20) performed a 2-back task of working memory, and the blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) response was used as a measure of neural activity during working memory performance.Though working memory performance did not differ significantly between ADHD adults and control subjects, ADHD adults showed significantly decreased activity in cerebellar and occipital regions and a trend toward decreased activation in an a priori predicted region of the prefrontal cortex.ADHD adults showed altered patterns of neural activity despite comparable performance on a verbal working memory task. These findings suggest that the cerebellum is involved in the pathophysiology of at least some cognitive deficits associated with ADHD and emphasize the need for additional research aimed at elucidating the role of the cerebellum in ADHD symptomatology.

Abstract

Verbal declarative memory is one of the most reliably impaired cognitive functions in schizophrenia. Important issues are whether the problem is reversible, and which brain regions underlie improvement. We showed previously that glucose administration improved declarative memory in patients with schizophrenia, and sought in this pilot study to identify whether glucose affects the location or degree of activation of brain regions involved in a verbal encoding task. Seven clinically stable and medicated patients with schizophrenia or schizoaffective disorder, who showed deficits on a clinical test of memory, participated in the study. Subjects served as their own controls in a double-blind, crossover protocol that consisted of two sessions about a week apart. In each session, subjects ingested a beverage flavored with lemonade that contained 50 g of glucose on one occasion, and saccharin on the other. Blood glucose was measured before and 15, 50, and 75 min after ingestion. After ingesting the beverage, they performed a verbal encoding task while undergoing brain functional magnetic resonance imaging. The results showed significantly greater activation of the left parahippocampus during novel sentence encoding in the glucose condition, compared to the saccharin condition, despite no change in memory performance. A trend towards greater activation of the left dorsolateral prefrontal cortex (p

Abstract

Studies of human classification learning using functional neuroimaging have suggested that basal ganglia and medial temporal lobe memory systems may interact during learning. We review these results and outline a set of possible mechanisms for such interactions. Effective connectivity analyses suggest that interaction between basal ganglia and medial temporal lobe are mediated by prefrontal cortex rather than by direct connectivity between regions. A review of possible neurobiological mechanisms suggests that interactions may be driven by neuromodulatory systems in addition to mediation by interaction of inputs to prefrontal cortical neurons. These results suggest that memory system interactions may reflect multiple mechanisms that combine to optimize behavior based on experience.

Abstract

Mesencephalic dopaminergic system (MDS) neurons may participate in learning by providing a prediction error signal to their targets, which include ventral striatal, orbital, and medial frontal regions, as well as by showing sensitivity to the degree of uncertainty associated with individual stimuli. We investigated the mechanisms of probabilistic classification learning in humans using functional magnetic resonance imaging to examine the effects of feedback and uncertainty. The design was optimized for separating neural responses to stimulus, delay, and negative and positive feedback components. Compared with fixation, stimulus and feedback activated brain regions consistent with the MDS, whereas the delay period did not. Midbrain activity was significantly different for negative versus positive feedback (consistent with coding of the "prediction error") and was reliably correlated with the degree of uncertainty as well as with activity in MDS target regions. Purely cognitive feedback apparently engages the same regions as rewarding stimuli, consistent with a broader characterization of this network.

Abstract

The striatum has been widely implicated in cognition, but a precise understanding of its role remains elusive. Here we present converging evidence for the role of the striatum in feedback-based learning. In a prior functional imaging study, healthy controls showed striatal activity during a feedback-based learning task, which was decreased when the same task was learned without feedback. In the present study, we show that individuals with striatal dysfunction due to Parkinson's disease are impaired on the feedback-based task, but not on a non-feedback version of the same task. Parkinson's patients and controls also used different learning strategies depending on feedback structure. This study provides direct behavioural evidence from humans that cortico-striatal systems are necessary for feedback-based learning on a cognitive task. These findings also link between learning impairments in Parkinson's disease and the physiological and computational evidence for the role of midbrain dopaminergic systems in feedback processing.

Abstract

It is controversial whether different cognitive functions can be mapped to discrete regions of the prefrontal cortex (PFC). The localisationist tradition has associated one cognitive function - inhibition - by turns with dorsolateral prefrontal cortex (DLPFC), inferior frontal cortex (IFC), or orbital frontal cortex (OFC). Inhibition is postulated to be a mechanism by which PFC exerts its effects on subcortical and posterior-cortical regions to implement executive control. We review evidence concerning inhibition of responses and task-sets. Whereas neuroimaging implicates diverse PFC foci, advances in human lesion-mapping support the functional localization of such inhibition to right IFC alone. Future research should investigate the generality of this proposed inhibitory function to other task domains, and its interaction within a wider network.

Abstract

First-degree relatives of persons with schizophrenia carry elevated genetic risk for the illness and show deficits on high-load information processing tasks. We used functional magnetic resonance imaging (fMRI) to test whether nonpsychotic relatives show altered functional activation in the prefrontal cortex (PFC), thalamus, hippocampus, and anterior cingulate during a working memory task requiring interference resolution.Twelve nonpsychotic relatives of persons with schizophrenia and 12 healthy control subjects were administered an auditory, verbal working memory version of the Continuous Performance Test during fMRI. An asymmetric, spin-echo, T2*-weighted sequence (15 contiguous, 7-mm axial slices) was acquired on a full-body MR scanner. Data were analyzed by Statistical Parametric Mapping (SPM).Compared with control subjects, relatives showed greater task-elicited activation in the PFC and the anterior and dorsomedial thalamus. When task performance was controlled, relatives showed significantly greater activation in the anterior cingulate. When effects of other potentially confounding variables were controlled, relatives generally showed significantly greater activation in the dorsomedial thalamus and anterior cingulate.This pilot study suggests that relatives of persons with schizophrenia have subtle differences in brain function in the absence of psychosis. These differences add to the growing literature identifying neurobiological vulnerabilities to schizophrenia.

Abstract

Functional magnetic resonance imaging (fMRI) in the pediatric population promises to provide novel insights into the nature of both normal and abnormal functional brain development as well as changes in brain function due to various interventions. Although acquisition of fMRI data from children is associated with a number of methodological challenges, primarily compliance and head motion, good quality data can be obtained. For example, conditioning and personal interactions can improve compliance, and motion reduction techniques can successfully reduce artifacts due to head motion. Analysis of pediatric fMRI data also involves challenges regarding spatial normalization and characterization of the hemodynamic response across development. Substantial progress has been made in understanding cognitive function and developmental disorders in children, but attention to the methodological issues raised in this review and continued investigations in this area are expected to result in further progress.

Abstract

Learning and memory in humans rely upon several memory systems, which appear to have dissociable brain substrates. A fundamental question concerns whether, and how, these memory systems interact. Here we show using functional magnetic resonance imaging (FMRI) that these memory systems may compete with each other during classification learning in humans. The medial temporal lobe and basal ganglia were differently engaged across subjects during classification learning depending upon whether the task emphasized declarative or nondeclarative memory, even when the to-be-learned material and the level of performance did not differ. Consistent with competition between memory systems suggested by animal studies and neuroimaging, activity in these regions was negatively correlated across individuals. Further examination of classification learning using event-related FMRI showed rapid modulation of activity in these regions at the beginning of learning, suggesting that subjects relied upon the medial temporal lobe early in learning. However, this dependence rapidly declined with training, as predicted by previous computational models of associative learning.

Abstract

Prefrontal cortex plays a central role in mnemonic control, with left inferior prefrontal cortex (LIPC) mediating control of semantic knowledge. One prominent theory posits that LIPC does not mediate semantic retrieval per se, but rather subserves the selection of task-relevant knowledge from amidst competing knowledge. The present event-related fMRI study provides evidence for an alternative hypothesis: LIPC guides controlled semantic retrieval irrespective of whether retrieval requires selection against competing representations. With selection demands held constant, LIPC activation increased with semantic retrieval demands and with the level of control required during retrieval. LIPC mediates a top-down bias signal that is recruited to the extent that the recovery of meaning demands controlled retrieval. Selection may reflect a specific instantiation of this mechanism.

Abstract

Previous research has suggested that perceptual fluency can contribute to recognition judgments. In this study, we examined whether fluency in recognition is based upon the speed of preceding operations, as suggested by studies of perceptual fluency. Subjects studied items in both lexical decision and naming tasks, and were then tested on two blocks of lexical decision trials with probe recognition trials. Jacoby's process dissociation procedure was used, and results from this procedure suggested that recognition judgments in the task were based largely upon familiarity. However, the estimated discriminability available from response time distributions was significantly less than the observed recognition discriminability. Simulated memory operating characteristics confirmed this under determination of recognition by response times. The results demonstrate, contrary to previous suggestions, that fluency in recognition is not based upon speed.

Abstract

A major area of research in memory and amnesia concerns the item specificity of implicit memory. In this paper we address several issues about the nature of implicit memory phenomena and about what constitutes an "item", using the procedural/declarative memory theory to guide us. We consider the nature of memory for items and of memory for relations among items, within the context of the procedural/declarative framework, providing us with the foundation necessary to analyse the basis for item-specific implicit memory phenomena. We review recent work from our laboratories demonstrating the fundamentally relational and flexible nature of declarative memory representation, in both humans and animals, and the essential role of the hippocampal system in relational memory processing. We show, further, that the memory representations supporting implicit memory phenomena are inflexible and nonrelational, and are tied to specific processing modules. Finally, we introduce empirical approaches that blur the distinction between skill learning and repetition priming, and show computational modelling results that demonstrate how these two implicit memory phenomena can be mediated by a single incremental learning mechanism, in accord with the claims of the procedural-declarative theory. Taken together, these various analyses of memory for items and memory for relations help to illuminate the nature of the functional deficit in amnesia and the memory systems of the brain.